Jon O’Donnell from Acuity Analytics and Pierre-Yves Gauthier from AlphaValue explore how AI is being measured, adopted and genuinely put to work inside financial research teams.
A big theme is that meaningful AI impact starts with measurement as before introducing any new AI capability, teams first establish a clear baseline of how long tasks take and how analysts perform without automation. That baseline becomes the reference point as each new iteration of an AI tool is then measured against it, making efficiency gains visible rather than anecdotal which creates a disciplined, almost scientific approach to proving value rather than assuming it.
AI adoption is treated as just as important as performance as usage is tracked weekly at an individual level, and that data feeds into targeted training and support with the idea is simple. Show people what good looks like, help them build confidence, and remove friction as over time, this ensures the tools don’t just exist, but are actually used. Detailed, page-by-page usage insights also allow products to be refined continuously, improving design based on real behaviour rather than assumptions.
On the research side, there’s recognition that some AI use cases, such as basic editorial consistency, are already fairly standard across the industry, but not always as stable or straightforward as they seem. The real leverage comes from using AI to “extend the wings” of analysts, helping them explore questions they wouldn’t normally have the time or data to answer.
A practical example brings this to life. Consider assessing how extreme weather events might affect a listed company with physical assets. Traditional corporate reporting often lacks detailed geographic insight. With the right prompts, AI can pull together asset locations, overlay weather patterns and build a practical risk map. For something like a hypermarket operator, this means quickly understanding how exposure varies store by store, and repeating that analysis across multiple companies.
The takeaway from AlphaValue and Acuity Analytics is that AI becomes most powerful when paired with human curiosity as tools can surface insights that companies don’t explicitly disclose, but they still rely on analysts who know which questions to ask and how to turn scattered signals into a clear, usable answer. That combination of structure, creativity and disciplined measurement is where AI starts to feel genuinely transformative for financial services.
